Research on the Design of Crop Disease Recognition System Based on Deep Learning

As an important economic pillar, agriculture plays a vital role in the development of China. However, Crop diseases and insect pests have always been the most common agricultural problems in agricultural production, which seriously affect the efficiency and quality of agricultural production. Therefore, how to identify effectively and control pests and diseases, so as to make the agricultural output increase well and quickly for farmers, is a topic of particular concern for farmers and researches at present. At present, there are still many deficiencies in the detection and control of agricultural pests and diseases in China. It is very important to strengthen the research on the detection and control of agricultural pests and diseases, improve the detection and control level of pests and diseases, and maintain the agricultural ecological balance. In view of this situation, this paper abandons traditional methods such as manual recognition and instrument recognition, then introduces a new generation of disease recognition methods based on deep learning, combined with image processing to the identification of crop pathological conditions, and obtains images based on the Internet of Things technology, which is an inevitable trend for the development of precision agriculture in the future. The results of this system design will be visualized, predictable and platformized to improve the utilization rate and usability of farmers.